K-means clustering

As explained in AI-Powered Search

K-means is an unsupervised clustering algorithm that partitions items into a specified number of groups by iteratively assigning items to and updating cluster centroids.

How AI-Powered Search covers it

How deep it goes

Provides Python fitting, labeling, visualization, and prediction, then recommends related top-N cosine clusters over a single assignment.

Examples

Uses product embeddings, cluster descriptions, visualized centroids, and the “microwave” query with five related predicted categories.

What it assumes

Assumes product and query embeddings plus familiarity with cosine similarity and category-based personalization.

What you can do after

Apply it via Python and scikit-learn to generate categories and map queries to clusters for search personalization.

How it connects

Connects embeddings, cosine similarity, semantic search, personalization, and dynamic categorization.

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Also explained in

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  liveBookmanning.com
  Data Without LabelsK-means clustering is an iterative clustering algorithm that partitions observations into a specified number of groups by minimizing within-group squared distance to cluster centroids.
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  Advanced Algorithms and Data StructuresK-means is an iterative clustering algorithm that partitions observations into a specified number of clusters by minimizing within-cluster squared distances to cluster centroids.
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  liveBookmanning.com
  Statistics Slam DunkK-means is a clustering algorithm that partitions observations into a prespecified number of groups by minimizing within-group squared distances to cluster centroids.
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  liveBookmanning.com
  Classic Computer Science Problems in Swift: Essential techniques for practicing programmersK-means clustering is an iterative unsupervised learning algorithm that partitions data points into a predefined number of clusters by repeatedly assigning points to their nearest centroid and recomputing the centroids.
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  liveBookmanning.com
  The Well-Grounded Data AnalystK-means is a clustering algorithm that partitions observations into a specified number of groups by minimizing distances from observations to their assigned cluster centers.
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  liveBookmanning.com
  Build AI-Enhanced Web AppsK-means clustering is an unsupervised clustering algorithm that assigns data points to a chosen number of groups according to their distances from cluster centers.

Compare how 7 books explain K-means clustering →

Related concepts

• centroiduses · A centroid is a central point representing a geometric figure or set of points, commonly calculated as the coordinate-wise mean, with weights when applicable.
• Euclidean distanceuses · Euclidean distance is the straight-line distance between two points in Euclidean space, calculated as the square root of the sum of squared differences between corresponding coordinates.
• clusteringproduces · Clustering is the technique of grouping data items according to similarity or shared characteristics.
• hierarchical clusteringcontrasts with · Hierarchical clustering is a clustering method that organizes observations into nested groups by successively merging or splitting clusters.
• unsupervised learningkind of · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
• hard clusteringkind of · Hard clustering assigns each observation to at most one cluster, with all clusters represented at the same hierarchy level.

This explainer was generated by Manning's knowledge graph from the text of AI-Powered Search. The section links point to the parts of the book it draws on.